Cooperative Multi-Agent Reinforcement Learning Method for Fast Voltage Regulation using Distributed Wind Turbine Generators

被引：0

作者：

Jimenez-Aparicio, Miguel ^{[1
]}

Darbali-Zamora, Rachid ^{[1
]}

机构：

[1] Sandia Natl Labs, POB 5800, Albuquerque, NM 87185 USA

来源：

2023 IEEE PES INNOVATIVE SMART GRID TECHNOLOGIES LATIN AMERICA, ISGT-LA | 2023年

关键词：

Voltage Regulation; DERMS; Wind Turbine Generator; Multi-Agent System; Reinforcement Learning;

D O I：

10.1109/ISGT-LA56058.2023.10328322

中图分类号：

X [环境科学、安全科学];

学科分类号：

08 ; 0830 ;

摘要：

This paper proposes a Multi-Agent Reinforcement Learning (MARL) voltage regulation method for distribution systems with presence of Wind Turbine Generators (WTGs). The control employs reactive power injection or absorption in three WTGs to regulate feeder voltages. A weighted global voltage metric is calculated to summarize nodes' voltages according to their relevance. Three independent RL Agents are trained using policy parameter sharing. Models are trained and tested on real-time Hardware-In-the-Loop setup. The proposed control effectively regulates voltage through the system, bringing median and mean voltages closer to 1 p.u. and reducing standard deviation.

引用

页码：215 / 219

页数：5

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